Paper: Collective Cross-Document Relation Extraction Without Labelled Data

ACL ID D10-1099
Title Collective Cross-Document Relation Extraction Without Labelled Data
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

We present a novel approach to relation ex- tractionthatintegratesinformationacrossdoc- uments, performs global inference and re- quires no labelled text. In particular, we tackle relation extraction and entity identifi- cation jointly. We use distant supervision to train a factor graph model for relation ex- traction based on an existing knowledge base (Freebase, derived in parts from Wikipedia). For inference we run an efficient Gibbs sam- pler that leads to linear time joint inference. We evaluate our approach both for an in- domain (Wikipedia) and a more realistic out- of-domain (New York Times Corpus) setting. For the in-domain setting, our joint model leads to 4% higher precision than an isolated local approach, but has no advantage over a pipeline. For the out-of-domain data, we ben...